Biomedical Informatics Tools for Applied Perioperative Physiology

应用围手术期生理学的生物医学信息学工具

• 批准号: 10376293
• 负责人: Maxime Cannesson
• 金额: $ 61.09万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10376293
• 关键词: Academia; Address; Adopted; Agreement; Algorithms; American; Anesthesia procedures; Anesthesiology; Animal Model; Area; Attention; Biological; Calibration; California; Cause of Death; Central venous pressure; Certified registered nurse anesthetist; Characteristics; Clinical; Clinical Medicine; Communities; Complex; Critical Care; Data; Data Collection; Data Set; Databases; Detection; Development; Dimensions; Documentation; Early Diagnosis; Electrocardiogram; Electronic Health Record; Event; Future; Gender; Goals; Hand; Health Personnel; Heart Arrest; Hospitals; Human; Incidence; Industry; Ingestion; Inpatients; Intensive Care Units; Israel; Knowledge; Lead; Los Angeles; Machine Learning; Measures; Medical center; Medicine; Melons; Minority Groups; Modeling; Monitor; Morbidity - disease rate; Operating Rooms; Operative Surgical Procedures; Patient Care; Patient Monitoring System; Patient-Focused Outcomes; Patients; Pattern; Perioperative; Physiologic Monitoring; Physiological; Physiology; Population Heterogeneity; Postoperative Period; Procedures; Process; Provider; Registries; Research; Research Personnel; Resources; Series; Shock; Source; Stress; System; Technology; Therapeutic Intervention; Time; Training; Twin Multiple Birth; Universities; Validation; Work; acute care; algorithm development; analytical tool; base; biomedical informatics; cardiovascular collapse; clinical decision support; clinical decision-making; clinically relevant; cloud based; computer science; data archive; data integration; data streams; deep learning; density; human data; human subject; improved; informatics tool; information display; interest; interoperability; large datasets; learning algorithm; machine learning algorithm; monitoring device; mortality; novel; predictive modeling; predictive signature; predictive tools; pressure; public database; relational database; sex; stressor; tool

Project Summary / Abstract Even though US hospitals have widely adopted electronic health record (EHR) documentation of patient care, interoperability of these systems remains an issue, leading to challenges in data integration. In the operating room (OR) setting, during surgery, physiological waveforms (arterial pressure, EKG, SpO2, central venous pressure, etc.) represent a large source of information used by clinical monitors to extract and display information in order for healthcare providers to make clinical decisions. Integration and synchronization of high-quality EHR and physiological waveform data in large datasets of surgical patients would allow machine learning and deep learning approaches to plumb these datasets for clinically relevant signatures that would promote advanced OR patient monitoring systems to define present state, predict state trajectory, suggest effective counter measures to minimize patients decompensated states, and define the usefulness and efficacy of new monitoring devices. The objective of this proposal is to focus the resources of an interdisciplinary team from academia (University of California Los Angeles (UCLA), University of California Irvine (UCI), and Carnegie Mellon University Computer Sciences), industry (Edwards Lifesciences Critical Care), and clinical medicine (anesthesiology, surgery, and critical care at UCLA, UCI, Beth Israel, and University of Pittsburgh Medical Center) to create, develop, and organize large surgical datasets combining EHR and high fidelity physiological waveform data, to make these datasets freely accessible, and to develop new predictive/forecasting monitoring systems for the surgical patients. The study will begin with the development of a machine learning algorithm to predict cardiovascular collapse during surgery. This algorithm development will be based on physiological signatures predictive of cardiovascular collapse identified in the animal models of shock. The study hypothesis is that the combination of two separate OR databases containing EHR and physiological waveforms will allow for training and development of monitoring solutions, predictive and/or prescriptive analytics tools, clinical decision support, and validate them on an independent, external validation database. The surgical setting is relevant because although 5.7 million Americans are admitted annually to an Intensive Care Unit, more than 50 million undergo surgery. OR databases are unique in medicine because: 1) Changes occur quickly and the lead-time before an event is compressed; 2) Knowledge of baseline/pre-stress status of surgical patients allows normalization, calibration, and markedly enhances prediction; 3) Continuous and immediate presence of dense skilled acute care practitioners allows faster implementation of complex treatment algorithms in the OR; and 4) Defined stages, procedures, and stressors allow building large common relational database registries. By helping to focus the provider's attention on significant events and changes in the patient's state and by suggesting physiological interpretations of that state, such systems will permit early detection of complex problems and provide guidance on therapeutic interventions improving patient outcomes.

项目总结/摘要 尽管美国医院已广泛采用电子健康记录（EHR）记录患者护理， 这些系统的互操作性仍然是一个问题，导致数据整合方面的挑战。在手术 手术室（OR）设置、手术期间、生理波形（动脉压、EKG、SpO 2、中心静脉 压力等）表示临床监视器用来提取和显示信息的大量信息源 以便医疗服务提供者做出临床决策。高质量EHR的集成和同步 手术患者的大型数据集中的生理波形数据将允许机器学习和深度 学习方法来探测这些数据集的临床相关特征，这些特征将促进高级OR 患者监测系统，用于定义当前状态，预测状态轨迹，建议有效的应对措施 以最大限度地减少患者的失代偿状态，并确定新监测设备的有用性和有效性。 这项建议的目的是集中学术界（大学）的跨学科小组的资源， 加州洛杉矶（UCLA）、加州尔湾大学（UCI）和卡内基梅隆大学计算机 科学）、工业（Edwards Lifesciences Critical Care）和临床医学（麻醉学、外科学和 加州大学洛杉矶分校，加州大学洛杉矶分校，贝丝以色列和匹兹堡大学医学中心的重症监护），以创建，开发， 组织结合EHR和高保真生理波形数据的大型手术数据集， 数据集免费访问，并开发新的预测/预报监测系统， 患者这项研究将开始，开发一种机器学习算法来预测心血管疾病。 在手术中崩溃该算法的开发将基于生理特征预测， 在休克动物模型中发现的心血管衰竭。研究假设是， 包含EHR和生理波形的两个单独的OR数据库将允许训练， 开发监测解决方案、预测性和/或规范性分析工具、临床决策支持，以及 在独立的外部验证数据库上验证它们。手术设置是相关的，因为虽然 5.7每年有1000万美国人进入重症监护室，超过5000万人接受手术。 手术室数据库在医学上是独一无二的，因为：1）变化发生得很快，事件发生前的提前期很短。 压缩; 2）手术患者的基线/预应力状态的知识允许标准化，校准， 并显着提高预测; 3）连续和立即存在密集熟练的急性护理 从业者允许在OR中更快地实现复杂的治疗算法;以及4）限定的阶段， 过程和压力源允许构建大型通用关系数据库注册表。通过帮助集中 提供者对患者状态中的重大事件和变化的关注，并通过建议生理 通过对这种状态的解释，这种系统将能够及早发现复杂的问题，并提供指导。 治疗干预改善病人的预后。